code: def export_adam_multiple() -> None:
      # Define operator attributes.
      norm_coefficient = 0.001
      alpha = 0.95
      beta = 0.85
      epsilon = 1e-2
  
      node = onnx.helper.make_node(
          "Adam",
          inputs=[
              "R",
              "T",
              "X1",
              "X2",
              "G1",
              "G2",
              "V1",
              "V2",
              "H1",
              "H2",
          ],
          outputs=[
              "X1_new",
              "X2_new",
              "V1_new",
              "V2_new",
              "H1_new",
              "H2_new",
          ],
          norm_coefficient=norm_coefficient,
          alpha=alpha,
          beta=beta,
          domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN,
      )
  
      # Define operator inputs.
      r = np.array(0.1, dtype=np.float32)  # scalar
      t = np.array(0, dtype=np.int64)  # scalar
  
      x1 = np.array([1.0], dtype=np.float32)
      g1 = np.array([-1.0], dtype=np.float32)
      v1 = np.array([2.0], dtype=np.float32)
      h1 = np.array([0.5], dtype=np.float32)
  
      x2 = np.array([1.0, 2.0], dtype=np.float32)
      g2 = np.array([-1.0, -3.0], dtype=np.float32)
      v2 = np.array([4.0, 1.0], dtype=np.float32)
      h2 = np.array([1.0, 10.0], dtype=np.float32)
  
      # Compute expected outputs of Adam.
      x1_new, v1_new, h1_new = apply_adam(
          r,
          t,
          x1,
          g1,
          v1,
          h1,
          norm_coefficient,
          0.0,
          alpha,
          beta,
          epsilon,
      )
      x2_new, v2_new, h2_new = apply_adam(
          r,
          t,
          x2,
          g2,
          v2,
          h2,
          norm_coefficient,
          0.0,
          alpha,
          beta,
          epsilon,
      )
  
      # Check results.
      expect(
          node,
          inputs=[r, t, x1, x2, g1, g2, v1, v2, h1, h2],
          outputs=[x1_new, x2_new, v1_new, v2_new, h1_new, h2_new],
          name="test_adam_multiple",
          opset_imports=[
              onnx.helper.make_opsetid(
                  AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1
              )
          ],
      )
  
file_path: onnx/onnx/backend/test/case/node/adam.py
imports: import numpy
  import onnx
  from onnx.backend.test.case.base import Base
  from onnx.backend.test.case.node import expect
  from onnx.defs import AI_ONNX_PREVIEW_TRAINING_DOMAIN
label_desc: Use image node_id 2 for calling the Adam obj's underlying member method code with example usage: obj.export_adam_multiple() without return types